Overview

Dataset statistics

Number of variables26
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.5%
Total size in memory448.5 KiB
Average record size in memory216.0 B

Variable types

Numeric20
Categorical6

Alerts

DR has constant value "0.0"Constant
Dataset has 11 (0.5%) duplicate rowsDuplicates
ALTV is highly overall correlated with MSTV and 3 other fieldsHigh correlation
ASTV is highly overall correlated with MSTVHigh correlation
CLASS is highly overall correlated with NSP and 1 other fieldsHigh correlation
DL is highly overall correlated with MSTV and 3 other fieldsHigh correlation
LB is highly overall correlated with LBE and 3 other fieldsHigh correlation
LBE is highly overall correlated with LB and 3 other fieldsHigh correlation
MSTV is highly overall correlated with ALTV and 6 other fieldsHigh correlation
Max is highly overall correlated with Nmax and 2 other fieldsHigh correlation
Mean is highly overall correlated with LB and 3 other fieldsHigh correlation
Median is highly overall correlated with LB and 3 other fieldsHigh correlation
Min is highly overall correlated with DL and 4 other fieldsHigh correlation
Mode is highly overall correlated with LB and 3 other fieldsHigh correlation
NSP is highly overall correlated with CLASS and 1 other fieldsHigh correlation
Nmax is highly overall correlated with MSTV and 4 other fieldsHigh correlation
SUSP is highly overall correlated with ALTV and 2 other fieldsHigh correlation
Variance is highly overall correlated with ALTV and 6 other fieldsHigh correlation
Width is highly overall correlated with ALTV and 6 other fieldsHigh correlation
DS is highly imbalanced (96.8%)Imbalance
DP is highly imbalanced (77.3%)Imbalance
SUSP is highly imbalanced (55.5%)Imbalance
AC has 891 (41.9%) zerosZeros
FM has 1311 (61.7%) zerosZeros
UC has 332 (15.6%) zerosZeros
ALTV has 1240 (58.3%) zerosZeros
MLTV has 137 (6.4%) zerosZeros
DL has 1231 (57.9%) zerosZeros
Nmax has 107 (5.0%) zerosZeros
Nzeros has 1624 (76.4%) zerosZeros
Variance has 187 (8.8%) zerosZeros

Reproduction

Analysis started2024-06-25 12:56:26.293372
Analysis finished2024-06-25 12:57:06.351568
Duration40.06 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

LBE
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:06.442387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2024-06-25T18:27:06.598637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

LB
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:06.727329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2024-06-25T18:27:06.884036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

AC
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7224835
Minimum0
Maximum26
Zeros891
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:07.009072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5608502
Coefficient of variation (CV)1.3079419
Kurtosis3.1224932
Mean2.7224835
Median Absolute Deviation (MAD)1
Skewness1.6588299
Sum5788
Variance12.679654
MonotonicityNot monotonic
2024-06-25T18:27:07.118441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 891
41.9%
1 242
 
11.4%
2 164
 
7.7%
3 162
 
7.6%
4 148
 
7.0%
5 110
 
5.2%
6 104
 
4.9%
7 76
 
3.6%
8 56
 
2.6%
9 50
 
2.4%
Other values (12) 123
 
5.8%
ValueCountFrequency (%)
0 891
41.9%
1 242
 
11.4%
2 164
 
7.7%
3 162
 
7.6%
4 148
 
7.0%
5 110
 
5.2%
6 104
 
4.9%
7 76
 
3.6%
8 56
 
2.6%
9 50
 
2.4%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
17 7
0.3%
16 4
 
0.2%
15 5
 
0.2%
14 13
0.6%
13 15
0.7%
12 17
0.8%

FM
Real number (ℝ)

ZEROS 

Distinct96
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2412982
Minimum0
Maximum564
Zeros1311
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:07.243441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile22
Maximum564
Range564
Interquartile range (IQR)2

Descriptive statistics

Standard deviation37.125309
Coefficient of variation (CV)5.1268858
Kurtosis104.63437
Mean7.2412982
Median Absolute Deviation (MAD)0
Skewness9.4274963
Sum15395
Variance1378.2886
MonotonicityNot monotonic
2024-06-25T18:27:07.384069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1311
61.7%
1 208
 
9.8%
2 119
 
5.6%
3 85
 
4.0%
4 65
 
3.1%
6 35
 
1.6%
7 28
 
1.3%
5 28
 
1.3%
8 25
 
1.2%
10 24
 
1.1%
Other values (86) 198
 
9.3%
ValueCountFrequency (%)
0 1311
61.7%
1 208
 
9.8%
2 119
 
5.6%
3 85
 
4.0%
4 65
 
3.1%
5 28
 
1.3%
6 35
 
1.6%
7 28
 
1.3%
8 25
 
1.2%
9 13
 
0.6%
ValueCountFrequency (%)
564 1
< 0.1%
557 1
< 0.1%
489 2
0.1%
443 1
< 0.1%
325 2
0.1%
324 1
< 0.1%
317 1
< 0.1%
314 1
< 0.1%
304 1
< 0.1%
290 1
< 0.1%

UC
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6599247
Minimum0
Maximum23
Zeros332
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:07.509067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8470935
Coefficient of variation (CV)0.7779104
Kurtosis1.289254
Mean3.6599247
Median Absolute Deviation (MAD)2
Skewness0.8353463
Sum7781
Variance8.1059415
MonotonicityNot monotonic
2024-06-25T18:27:07.602815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 332
15.6%
3 294
13.8%
4 272
12.8%
1 238
11.2%
2 236
11.1%
5 235
11.1%
6 199
9.4%
7 114
 
5.4%
8 82
 
3.9%
9 57
 
2.7%
Other values (9) 67
 
3.2%
ValueCountFrequency (%)
0 332
15.6%
1 238
11.2%
2 236
11.1%
3 294
13.8%
4 272
12.8%
5 235
11.1%
6 199
9.4%
7 114
 
5.4%
8 82
 
3.9%
9 57
 
2.7%
ValueCountFrequency (%)
23 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 3
 
0.1%
13 6
 
0.3%
12 9
 
0.4%
11 16
 
0.8%
10 29
1.4%
9 57
2.7%

ASTV
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.990122
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:07.745672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.192814
Coefficient of variation (CV)0.36588144
Kurtosis-1.0510296
Mean46.990122
Median Absolute Deviation (MAD)14
Skewness-0.011828576
Sum99901
Variance295.59284
MonotonicityNot monotonic
2024-06-25T18:27:07.889838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 62
 
2.9%
58 61
 
2.9%
65 60
 
2.8%
63 58
 
2.7%
64 58
 
2.7%
61 57
 
2.7%
51 54
 
2.5%
62 51
 
2.4%
22 48
 
2.3%
25 46
 
2.2%
Other values (65) 1571
73.9%
ValueCountFrequency (%)
12 2
 
0.1%
13 7
 
0.3%
14 4
 
0.2%
15 4
 
0.2%
16 12
 
0.6%
17 13
 
0.6%
18 10
 
0.5%
19 19
0.9%
20 27
1.3%
21 33
1.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 4
 
0.2%
84 6
 
0.3%
83 4
 
0.2%
82 2
 
0.1%
81 7
 
0.3%
80 7
 
0.3%
79 15
0.7%
78 19
0.9%
77 16
0.8%

MSTV
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3327846
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:08.046561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88324133
Coefficient of variation (CV)0.66270375
Kurtosis4.7007563
Mean1.3327846
Median Absolute Deviation (MAD)0.5
Skewness1.6573392
Sum2833.5
Variance0.78011525
MonotonicityNot monotonic
2024-06-25T18:27:08.187160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 125
 
5.9%
1.3 121
 
5.7%
0.5 121
 
5.7%
0.4 120
 
5.6%
0.7 117
 
5.5%
0.9 114
 
5.4%
0.6 113
 
5.3%
1.2 107
 
5.0%
1.5 100
 
4.7%
1 99
 
4.7%
Other values (47) 989
46.5%
ValueCountFrequency (%)
0.2 47
 
2.2%
0.3 84
4.0%
0.4 120
5.6%
0.5 121
5.7%
0.6 113
5.3%
0.7 117
5.5%
0.8 125
5.9%
0.9 114
5.4%
1 99
4.7%
1.1 97
4.6%
ValueCountFrequency (%)
7 1
< 0.1%
6.9 1
< 0.1%
6.3 2
0.1%
6 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%
5.4 2
0.1%
5.3 1
< 0.1%
5.2 1
< 0.1%
5 2
0.1%

ALTV
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8466604
Minimum0
Maximum91
Zeros1240
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:08.327810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.39688
Coefficient of variation (CV)1.868337
Kurtosis4.2529979
Mean9.8466604
Median Absolute Deviation (MAD)0
Skewness2.1950753
Sum20934
Variance338.44518
MonotonicityNot monotonic
2024-06-25T18:27:08.468440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
5 43
 
2.0%
4 40
 
1.9%
3 36
 
1.7%
8 34
 
1.6%
6 31
 
1.5%
12 29
 
1.4%
10 23
 
1.1%
Other values (77) 553
26.0%
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
3 36
 
1.7%
4 40
 
1.9%
5 43
 
2.0%
6 31
 
1.5%
7 23
 
1.1%
8 34
 
1.6%
9 22
 
1.0%
ValueCountFrequency (%)
91 4
0.2%
90 2
 
0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 6
0.3%
82 1
 
< 0.1%
81 2
 
0.1%
79 1
 
< 0.1%
78 3
0.1%

MLTV
Real number (ℝ)

ZEROS 

Distinct249
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1876294
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:08.609065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.475
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.6282466
Coefficient of variation (CV)0.68740857
Kurtosis4.1312538
Mean8.1876294
Median Absolute Deviation (MAD)3.1
Skewness1.3319979
Sum17406.9
Variance31.67716
MonotonicityNot monotonic
2024-06-25T18:27:08.750539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
6.4%
7.1 29
 
1.4%
6.7 29
 
1.4%
6.5 25
 
1.2%
5.2 25
 
1.2%
9.5 24
 
1.1%
6.8 23
 
1.1%
5.6 23
 
1.1%
7.2 23
 
1.1%
8.5 23
 
1.1%
Other values (239) 1765
83.0%
ValueCountFrequency (%)
0 137
6.4%
0.1 4
 
0.2%
0.2 4
 
0.2%
0.3 9
 
0.4%
0.4 6
 
0.3%
0.5 11
 
0.5%
0.6 3
 
0.1%
0.7 4
 
0.2%
0.8 1
 
< 0.1%
0.9 5
 
0.2%
ValueCountFrequency (%)
50.7 1
< 0.1%
41.8 1
< 0.1%
40.8 1
< 0.1%
36.9 1
< 0.1%
35.7 1
< 0.1%
34.7 1
< 0.1%
33.5 1
< 0.1%
29.6 1
< 0.1%
29.5 1
< 0.1%
29.3 1
< 0.1%

DL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5700847
Minimum0
Maximum16
Zeros1231
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:08.859909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4992288
Coefficient of variation (CV)1.5917796
Kurtosis3.1468464
Mean1.5700847
Median Absolute Deviation (MAD)0
Skewness1.8191195
Sum3338
Variance6.2461447
MonotonicityNot monotonic
2024-06-25T18:27:08.984912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1231
57.9%
1 232
 
10.9%
2 127
 
6.0%
4 123
 
5.8%
3 112
 
5.3%
5 108
 
5.1%
6 67
 
3.2%
7 45
 
2.1%
8 28
 
1.3%
9 25
 
1.2%
Other values (5) 28
 
1.3%
ValueCountFrequency (%)
0 1231
57.9%
1 232
 
10.9%
2 127
 
6.0%
3 112
 
5.3%
4 123
 
5.8%
5 108
 
5.1%
6 67
 
3.2%
7 45
 
2.1%
8 28
 
1.3%
9 25
 
1.2%
ValueCountFrequency (%)
16 1
 
< 0.1%
14 2
 
0.1%
12 6
 
0.3%
11 12
 
0.6%
10 7
 
0.3%
9 25
 
1.2%
8 28
 
1.3%
7 45
2.1%
6 67
3.2%
5 108
5.1%

DS
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
0.0
2119 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2119
99.7%
1.0 7
 
0.3%

Length

2024-06-25T18:27:09.110382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:09.219731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2119
99.7%
1.0 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4245
99.8%
1 7
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4245
66.6%
. 2126
33.3%
1 7
 
0.1%

DP
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
0.0
1948 
1.0
 
109
2.0
 
49
3.0
 
19
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1948
91.6%
1.0 109
 
5.1%
2.0 49
 
2.3%
3.0 19
 
0.9%
4.0 1
 
< 0.1%

Length

2024-06-25T18:27:09.313506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:09.422880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1948
91.6%
1.0 109
 
5.1%
2.0 49
 
2.3%
3.0 19
 
0.9%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4074
95.8%
1 109
 
2.6%
2 49
 
1.2%
3 19
 
0.4%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4074
63.9%
. 2126
33.3%
1 109
 
1.7%
2 49
 
0.8%
3 19
 
0.3%
4 1
 
< 0.1%

DR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
0.0
2126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2126
100.0%

Length

2024-06-25T18:27:09.532256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:09.641609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2126
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4252
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4252
66.7%
. 2126
33.3%

Width
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.445908
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:09.802470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median67.5
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.955693
Coefficient of variation (CV)0.55298731
Kurtosis-0.90228678
Mean70.445908
Median Absolute Deviation (MAD)31.5
Skewness0.31423475
Sum149768
Variance1517.546
MonotonicityNot monotonic
2024-06-25T18:27:09.943116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 42
 
2.0%
102 35
 
1.6%
27 30
 
1.4%
31 29
 
1.4%
90 28
 
1.3%
98 28
 
1.3%
96 27
 
1.3%
83 27
 
1.3%
22 27
 
1.3%
42 26
 
1.2%
Other values (144) 1827
85.9%
ValueCountFrequency (%)
3 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 10
0.5%
9 6
 
0.3%
10 9
0.4%
11 10
0.5%
12 20
0.9%
13 13
0.6%
ValueCountFrequency (%)
180 1
 
< 0.1%
176 6
0.3%
163 2
 
0.1%
162 1
 
< 0.1%
161 5
0.2%
158 2
 
0.1%
153 3
 
0.1%
150 10
0.5%
149 11
0.5%
148 8
0.4%

Min
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579492
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:10.099365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.560212
Coefficient of variation (CV)0.31588344
Kurtosis-1.2904222
Mean93.579492
Median Absolute Deviation (MAD)27
Skewness0.11578402
Sum198950
Variance873.80615
MonotonicityNot monotonic
2024-06-25T18:27:10.256095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 77
 
3.6%
52 50
 
2.4%
71 49
 
2.3%
120 48
 
2.3%
60 45
 
2.1%
68 43
 
2.0%
67 41
 
1.9%
103 40
 
1.9%
51 36
 
1.7%
62 35
 
1.6%
Other values (99) 1662
78.2%
ValueCountFrequency (%)
50 77
3.6%
51 36
1.7%
52 50
2.4%
53 32
1.5%
54 27
 
1.3%
55 20
 
0.9%
56 19
 
0.9%
57 22
 
1.0%
58 22
 
1.0%
59 17
 
0.8%
ValueCountFrequency (%)
159 1
 
< 0.1%
158 1
 
< 0.1%
156 1
 
< 0.1%
155 2
 
0.1%
154 3
 
0.1%
153 8
0.4%
152 4
0.2%
151 4
0.2%
150 3
 
0.1%
149 2
 
0.1%

Max
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0254
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:10.459221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.944183
Coefficient of variation (CV)0.10939881
Kurtosis0.63276948
Mean164.0254
Median Absolute Deviation (MAD)11
Skewness0.57786245
Sum348718
Variance321.99371
MonotonicityNot monotonic
2024-06-25T18:27:10.631097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 71
 
3.3%
171 66
 
3.1%
158 62
 
2.9%
156 60
 
2.8%
159 58
 
2.7%
152 54
 
2.5%
154 52
 
2.4%
178 52
 
2.4%
172 48
 
2.3%
153 48
 
2.3%
Other values (76) 1555
73.1%
ValueCountFrequency (%)
122 2
 
0.1%
123 2
 
0.1%
125 3
 
0.1%
126 5
0.2%
127 2
 
0.1%
128 4
 
0.2%
129 10
0.5%
130 8
0.4%
131 7
0.3%
132 4
 
0.2%
ValueCountFrequency (%)
238 6
 
0.3%
230 3
 
0.1%
228 5
 
0.2%
213 1
 
< 0.1%
211 5
 
0.2%
210 4
 
0.2%
205 1
 
< 0.1%
204 3
 
0.1%
200 31
1.5%
199 20
0.9%

Nmax
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0682032
Minimum0
Maximum18
Zeros107
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:10.757573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9493856
Coefficient of variation (CV)0.72498483
Kurtosis0.50421053
Mean4.0682032
Median Absolute Deviation (MAD)2
Skewness0.89288591
Sum8649
Variance8.6988755
MonotonicityNot monotonic
2024-06-25T18:27:11.023171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
0 107
 
5.0%
8 106
 
5.0%
9 67
 
3.2%
Other values (8) 118
 
5.6%
ValueCountFrequency (%)
0 107
 
5.0%
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
8 106
 
5.0%
9 67
 
3.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
0.1%
15 1
 
< 0.1%
14 5
 
0.2%
13 10
 
0.5%
12 22
 
1.0%
11 28
 
1.3%
10 49
2.3%
9 67
3.2%
8 106
5.0%

Nzeros
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32361242
Minimum0
Maximum10
Zeros1624
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:11.132573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70605937
Coefficient of variation (CV)2.1818056
Kurtosis30.365084
Mean0.32361242
Median Absolute Deviation (MAD)0
Skewness3.9202874
Sum688
Variance0.49851984
MonotonicityNot monotonic
2024-06-25T18:27:11.242434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 2
 
0.1%
4 2
 
0.1%
3 21
 
1.0%
2 108
 
5.1%
1 366
 
17.2%
0 1624
76.4%

Mode
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.45202
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:11.367435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.25
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.381289
Coefficient of variation (CV)0.11917823
Kurtosis3.0095305
Mean137.45202
Median Absolute Deviation (MAD)10
Skewness-0.99517784
Sum292223
Variance268.34664
MonotonicityNot monotonic
2024-06-25T18:27:11.508056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 140
 
6.6%
136 89
 
4.2%
150 89
 
4.2%
142 87
 
4.1%
148 79
 
3.7%
144 78
 
3.7%
129 76
 
3.6%
143 71
 
3.3%
125 67
 
3.2%
126 66
 
3.1%
Other values (78) 1284
60.4%
ValueCountFrequency (%)
60 6
0.3%
67 5
0.2%
69 1
 
< 0.1%
71 1
 
< 0.1%
75 6
0.3%
76 1
 
< 0.1%
77 1
 
< 0.1%
86 11
0.5%
88 6
0.3%
89 3
 
0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
186 6
0.3%
180 4
 
0.2%
179 1
 
< 0.1%
176 6
0.3%
170 4
 
0.2%
169 3
 
0.1%
167 8
0.4%
165 10
0.5%
164 1
 
< 0.1%

Mean
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.61054
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:11.664300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.593596
Coefficient of variation (CV)0.11584232
Kurtosis0.93342749
Mean134.61054
Median Absolute Deviation (MAD)10
Skewness-0.65101924
Sum286182
Variance243.16025
MonotonicityNot monotonic
2024-06-25T18:27:11.825357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 65
 
3.1%
144 64
 
3.0%
135 63
 
3.0%
141 61
 
2.9%
140 60
 
2.8%
132 59
 
2.8%
133 58
 
2.7%
145 58
 
2.7%
136 57
 
2.7%
147 57
 
2.7%
Other values (93) 1524
71.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 2
0.1%
81 1
 
< 0.1%
82 2
0.1%
83 4
0.2%
84 3
0.1%
ValueCountFrequency (%)
182 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
173 2
0.1%
172 1
< 0.1%
171 2
0.1%
170 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%

Median
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.09031
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:11.965981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.466589
Coefficient of variation (CV)0.1047618
Kurtosis0.66725933
Mean138.09031
Median Absolute Deviation (MAD)10
Skewness-0.4784142
Sum293580
Variance209.28219
MonotonicityNot monotonic
2024-06-25T18:27:12.122233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 69
 
3.2%
137 68
 
3.2%
142 68
 
3.2%
145 67
 
3.2%
147 65
 
3.1%
151 63
 
3.0%
141 63
 
3.0%
134 62
 
2.9%
149 60
 
2.8%
143 56
 
2.6%
Other values (85) 1485
69.8%
ValueCountFrequency (%)
77 1
< 0.1%
78 1
< 0.1%
79 2
0.1%
82 1
< 0.1%
86 1
< 0.1%
87 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
92 2
0.1%
93 1
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
0.1%
174 2
0.1%
172 1
< 0.1%
171 1
< 0.1%
170 2
0.1%

Variance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.80809
Minimum0
Maximum269
Zeros187
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:12.263297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.977636
Coefficient of variation (CV)1.5407006
Kurtosis15.131589
Mean18.80809
Median Absolute Deviation (MAD)6
Skewness3.2199738
Sum39986
Variance839.70339
MonotonicityNot monotonic
2024-06-25T18:27:12.419585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 248
 
11.7%
0 187
 
8.8%
2 166
 
7.8%
3 161
 
7.6%
4 108
 
5.1%
5 85
 
4.0%
8 74
 
3.5%
6 65
 
3.1%
7 53
 
2.5%
9 49
 
2.3%
Other values (123) 930
43.7%
ValueCountFrequency (%)
0 187
8.8%
1 248
11.7%
2 166
7.8%
3 161
7.6%
4 108
5.1%
5 85
 
4.0%
6 65
 
3.1%
7 53
 
2.5%
8 74
 
3.5%
9 49
 
2.3%
ValueCountFrequency (%)
269 1
< 0.1%
254 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
241 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
182 1
< 0.1%
177 1
< 0.1%

Tendency
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
0.0
1115 
1.0
846 
-1.0
165 

Length

Max length4
Median length3
Mean length3.0776105
Min length3

Characters and Unicode

Total characters6543
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1115
52.4%
1.0 846
39.8%
-1.0 165
 
7.8%

Length

2024-06-25T18:27:12.544579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:12.653961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1115
52.4%
1.0 1011
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
65.0%
Other Punctuation 2126
32.5%
Dash Punctuation 165
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3241
76.2%
1 1011
 
23.8%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

SUSP
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
0.0
1929 
1.0
197 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1929
90.7%
1.0 197
 
9.3%

Length

2024-06-25T18:27:12.763064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:12.856784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1929
90.7%
1.0 197
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4055
95.4%
1 197
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4055
63.6%
. 2126
33.3%
1 197
 
3.1%

CLASS
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5098777
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.2 KiB
2024-06-25T18:27:12.950565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0268829
Coefficient of variation (CV)0.6711674
Kurtosis-1.2290404
Mean4.5098777
Median Absolute Deviation (MAD)2
Skewness0.38116341
Sum9588
Variance9.16202
MonotonicityNot monotonic
2024-06-25T18:27:13.059935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 579
27.2%
1 384
18.1%
6 332
15.6%
7 252
11.9%
10 197
 
9.3%
8 107
 
5.0%
4 81
 
3.8%
5 72
 
3.4%
9 69
 
3.2%
3 53
 
2.5%
ValueCountFrequency (%)
1 384
18.1%
2 579
27.2%
3 53
 
2.5%
4 81
 
3.8%
5 72
 
3.4%
6 332
15.6%
7 252
11.9%
8 107
 
5.0%
9 69
 
3.2%
10 197
 
9.3%
ValueCountFrequency (%)
10 197
 
9.3%
9 69
 
3.2%
8 107
 
5.0%
7 252
11.9%
6 332
15.6%
5 72
 
3.4%
4 81
 
3.8%
3 53
 
2.5%
2 579
27.2%
1 384
18.1%

NSP
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
1.0
1655 
2.0
295 
3.0
176 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Length

2024-06-25T18:27:13.169315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-25T18:27:13.263059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2126
50.0%
1 1655
38.9%
2 295
 
6.9%
3 176
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Interactions

2024-06-25T18:27:03.828333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.283657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.149481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.001045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.884559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.883684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.695189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.718029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.781716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.582734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.443941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.565144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.493287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.342496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.240546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.340429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.191194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.088919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.095514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.960747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.906487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.377409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.228118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.079198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.974385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.973471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.784841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.808278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.875464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.676482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.537696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.658881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.577953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.436245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.334295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.434198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.284944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.182703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.189234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.038891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.000230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.474420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.321837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.172918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.068105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.063220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.878596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.902440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.953588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.773788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.631443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.754964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.665599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.529967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.428045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.512336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.378662image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.276341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.282985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.132609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.093957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.568059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.415588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.266669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.146263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.151376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.987967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.001932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.047761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.851943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.732380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.848715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.763644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.623719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.521795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.606439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.472412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.370123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.361141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.226360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.172117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.646183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.512220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.355867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.239983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.230132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.081716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.102133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.141533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.945658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.826160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.942861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.857392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.706122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.615579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.703334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.566163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.463870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.455308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.320110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.265832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.740901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.590316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.439003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.443135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.308261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.175498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.197525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.219642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.039409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.919910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.021011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.935515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.799643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.713188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.780848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.644317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.541965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.549090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.413860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.359613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.834690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.701085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.534714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.559125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.417631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.269248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.306040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.313423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.133193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.029258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.130357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.044889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.909017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.821989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.890236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.757349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.651370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.642838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.507610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.453331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:27.944032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.793808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.644090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.652876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.520404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.378620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.409568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.407142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.227448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.123002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.239729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.138644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.002767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.931845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.983976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.851096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.749808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.741337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.617412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.531486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.022158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.871928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.722635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.744252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.598078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.472344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.500355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.485299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.321162image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.216786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.317852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.216800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.096909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.166225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.062496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.944817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.827938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.834517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.698743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.625240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.115939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.965678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.816412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.822410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.694086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.573836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.597463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.572828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.414912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.310508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.411635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.310520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.190658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.259971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.156243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.038597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.921689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.928234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.793604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.729918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.210146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.059395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:31.925789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:33.931787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.797566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.679875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.825167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.684271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.508665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.420272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.521473image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.419894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.284441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.353753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.250381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.147942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.031099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.022017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.887360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.816161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.319508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.168805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.019542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.025508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.891317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.772909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:39.934537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.785370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.610701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.529647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.625854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.513640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.393785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.463096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.359756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.242194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.124814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.131384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:02.981107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:04.909878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.397640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.246927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.124719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.119258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:35.969476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.867145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.028775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.863492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.709147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.619108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.712120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.602196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.487533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.556876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.453507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.335915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.218596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.209484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.074856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.003664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.507374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.356758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.223648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.213008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.063225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:37.976517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.122525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:41.957242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.792787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.736226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.821413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.702445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.586413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.664195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.547258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.429663image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.312313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.303265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.168573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.097380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.601154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.450537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.325287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.320356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.156968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.085866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.216309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.051027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.896557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.845595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:47.915194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.795592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.688648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.761839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.650766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.539040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.406507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.412639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.277950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.191128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.697458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.544258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.418417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.414445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.250697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.179619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.310026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.145267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:43.990307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:45.939350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.008935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.889339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.771436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.855618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.737608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.636601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.500240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.506753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.356105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.269285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.789714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.638007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.515352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.511401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.344474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.273398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.404163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.239016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.084054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.033132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.118286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:49.983088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.881170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:53.958387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.831361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.729539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.593992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.600503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.465451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.363030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.867869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.732294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.609496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.606271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.422599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.367119image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.497914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.317107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.177806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.126874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.212040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.076839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:51.974920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.059179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:55.925108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.823325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.829892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.694615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.543607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.456787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:28.961621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.813580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.693959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.701276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.516350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.492115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.591697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.410859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.268984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.220595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.305792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.170588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.068677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.152933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.018864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:57.917046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:59.923611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.773270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.637770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:05.550505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:29.055370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:30.907295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:32.796525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:34.783471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:36.610107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:38.617113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:40.699463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:42.504609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:44.365783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:46.471381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:48.399539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:50.248712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:52.146795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:54.246713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:56.113031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:26:58.010795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:00.017363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:01.867017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-25T18:27:03.735165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-06-25T18:27:13.372913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ACALTVASTVCLASSDLDPDSFMLBLBEMLTVMSTVMaxMeanMedianMinModeNSPNmaxNzerosSUSPTendencyUCVarianceWidth
AC1.000-0.473-0.316-0.2330.0410.0000.0000.076-0.112-0.112-0.0870.3260.4720.1970.204-0.1730.1820.2740.249-0.0000.2470.0610.2370.3960.359
ALTV-0.4731.0000.4250.066-0.3830.0530.000-0.0800.3400.340-0.043-0.685-0.3210.2920.2450.4650.2220.479-0.359-0.1650.5360.027-0.249-0.650-0.526
ASTV-0.3160.4251.0000.244-0.1240.1350.1140.2630.3170.317-0.338-0.521-0.1260.1370.1640.2690.1260.453-0.166-0.1740.4670.157-0.116-0.345-0.279
CLASS-0.2330.0660.2441.0000.4370.4150.2030.1460.1140.114-0.2630.0900.103-0.142-0.075-0.215-0.0280.9640.1910.1240.9980.254-0.0740.2720.211
DL0.041-0.383-0.1240.4371.0000.1890.2620.058-0.168-0.168-0.2530.6110.245-0.459-0.314-0.598-0.2390.2030.4820.2670.2010.0920.3540.7090.586
DP0.0000.0530.1350.4150.1891.0000.0440.121-0.142-0.142-0.2380.3020.106-0.405-0.358-0.311-0.3110.4260.2420.0800.0860.1680.1490.3790.288
DS0.0000.0000.1140.2030.2620.0441.0000.003-0.060-0.060-0.0380.052-0.022-0.091-0.087-0.074-0.0980.1590.0160.0620.0000.1350.0220.0920.051
FM0.076-0.0800.2630.1460.0580.1210.0031.000-0.024-0.024-0.1170.0820.125-0.081-0.039-0.189-0.0330.1200.203-0.0950.0000.063-0.2310.1270.200
LB-0.1120.3400.3170.114-0.168-0.142-0.060-0.0241.0001.000-0.055-0.3670.3260.7880.8410.3580.8180.295-0.121-0.0650.3130.275-0.105-0.239-0.155
LBE-0.1120.3400.3170.114-0.168-0.142-0.060-0.0241.0001.000-0.055-0.3670.3260.7880.8410.3580.8180.295-0.121-0.0650.3130.275-0.105-0.239-0.155
MLTV-0.087-0.043-0.338-0.263-0.253-0.238-0.038-0.117-0.055-0.0551.000-0.020-0.0460.0770.011-0.0810.0020.220-0.0020.0720.1500.101-0.093-0.0930.048
MSTV0.326-0.685-0.5210.0900.6110.3020.0520.082-0.367-0.367-0.0201.0000.372-0.460-0.359-0.695-0.3130.3330.5520.2960.4210.0560.3010.7790.714
Max0.472-0.321-0.1260.1030.2450.106-0.0220.1250.3260.326-0.0460.3721.0000.3420.413-0.2920.4120.1430.5020.1680.1910.1700.1510.5270.649
Mean0.1970.2920.137-0.142-0.459-0.405-0.091-0.0810.7880.7880.077-0.4600.3421.0000.9580.4710.9080.496-0.191-0.1160.2450.306-0.138-0.325-0.234
Median0.2040.2450.164-0.075-0.314-0.358-0.087-0.0390.8410.8410.011-0.3590.4130.9581.0000.3870.9610.434-0.106-0.0740.2130.355-0.086-0.216-0.135
Min-0.1730.4650.269-0.215-0.598-0.311-0.074-0.1890.3580.358-0.081-0.695-0.2920.4710.3871.0000.3460.313-0.700-0.3270.3710.214-0.104-0.751-0.905
Mode0.1820.2220.126-0.028-0.239-0.311-0.098-0.0330.8180.8180.002-0.3130.4120.9080.9610.3461.0000.433-0.072-0.0730.1920.370-0.051-0.152-0.103
NSP0.2740.4790.4530.9640.2030.4260.1590.1200.2950.2950.2200.3330.1430.4960.4340.3130.4331.000-0.081-0.0490.7910.169-0.243-0.174-0.149
Nmax0.249-0.359-0.1660.1910.4820.2420.0160.203-0.121-0.121-0.0020.5520.502-0.191-0.106-0.700-0.072-0.0811.0000.2930.2110.1420.1350.6470.775
Nzeros-0.000-0.165-0.1740.1240.2670.0800.062-0.095-0.065-0.0650.0720.2960.168-0.116-0.074-0.327-0.073-0.0490.2931.0000.0530.0400.0420.3020.332
SUSP0.2470.5360.4670.9980.2010.0860.0000.0000.3130.3130.1500.4210.1910.2450.2130.3710.1920.7910.2110.0531.0000.051-0.278-0.391-0.296
Tendency0.0610.0270.1570.2540.0920.1680.1350.0630.2750.2750.1010.0560.1700.3060.3550.2140.3700.1690.1420.0400.0511.000-0.0670.0660.159
UC0.237-0.249-0.116-0.0740.3540.1490.022-0.231-0.105-0.105-0.0930.3010.151-0.138-0.086-0.104-0.051-0.2430.1350.042-0.278-0.0671.0000.2820.157
Variance0.396-0.650-0.3450.2720.7090.3790.0920.127-0.239-0.239-0.0930.7790.527-0.325-0.216-0.751-0.152-0.1740.6470.302-0.3910.0660.2821.0000.830
Width0.359-0.526-0.2790.2110.5860.2880.0510.200-0.155-0.1550.0480.7140.649-0.234-0.135-0.905-0.103-0.1490.7750.332-0.2960.1590.1570.8301.000

Missing values

2024-06-25T18:27:05.713249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-25T18:27:06.210943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencySUSPCLASSNSP
0120.0120.00.00.00.073.00.543.02.40.00.00.00.064.062.0126.02.00.0120.0137.0121.073.01.00.09.02.0
1132.0132.04.00.04.017.02.10.010.42.00.00.00.0130.068.0198.06.01.0141.0136.0140.012.00.00.06.01.0
2133.0133.02.00.05.016.02.10.013.42.00.00.00.0130.068.0198.05.01.0141.0135.0138.013.00.00.06.01.0
3134.0134.02.00.06.016.02.40.023.02.00.00.00.0117.053.0170.011.00.0137.0134.0137.013.01.00.06.01.0
4132.0132.04.00.05.016.02.40.019.90.00.00.00.0117.053.0170.09.00.0137.0136.0138.011.01.00.02.01.0
5134.0134.01.00.010.026.05.90.00.09.00.02.00.0150.050.0200.05.03.076.0107.0107.0170.00.00.08.03.0
6134.0134.01.00.09.029.06.30.00.06.00.02.00.0150.050.0200.06.03.071.0107.0106.0215.00.00.08.03.0
7122.0122.00.00.00.083.00.56.015.60.00.00.00.068.062.0130.00.00.0122.0122.0123.03.01.00.09.03.0
8122.0122.00.00.01.084.00.55.013.60.00.00.00.068.062.0130.00.00.0122.0122.0123.03.01.00.09.03.0
9122.0122.00.00.03.086.00.36.010.60.00.00.00.068.062.0130.01.00.0122.0122.0123.01.01.00.09.03.0
LBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencySUSPCLASSNSP
2116140.0140.01.00.01.080.00.236.02.20.00.00.00.018.0140.0158.01.00.0147.0148.0149.01.00.00.02.01.0
2117140.0140.00.00.06.079.00.320.08.50.00.00.00.026.0124.0150.01.00.0144.0143.0145.01.01.00.01.01.0
2118140.0140.00.00.07.079.00.526.07.01.00.00.00.021.0129.0150.01.00.0145.0142.0145.02.01.00.01.01.0
2119140.0140.00.00.06.079.00.627.06.41.00.00.00.026.0124.0150.01.00.0144.0141.0145.01.01.00.01.01.0
2120140.0140.00.00.04.077.00.717.06.01.00.00.00.031.0124.0155.02.00.0145.0143.0145.02.00.00.01.01.0
2121140.0140.00.00.06.079.00.225.07.20.00.00.00.040.0137.0177.04.00.0153.0150.0152.02.00.00.05.02.0
2122140.0140.01.00.09.078.00.422.07.10.00.00.00.066.0103.0169.06.00.0152.0148.0151.03.01.00.05.02.0
2123140.0140.01.00.07.079.00.420.06.10.00.00.00.067.0103.0170.05.00.0153.0148.0152.04.01.00.05.02.0
2124140.0140.01.00.09.078.00.427.07.00.00.00.00.066.0103.0169.06.00.0152.0147.0151.04.01.00.05.02.0
2125142.0142.01.01.05.074.00.436.05.00.00.00.00.042.0117.0159.02.01.0145.0143.0145.01.00.00.01.01.0

Duplicate rows

Most frequently occurring

LBELBACFMUCASTVMSTVALTVMLTVDLDSDPDRWidthMinMaxNmaxNzerosModeMeanMedianVarianceTendencySUSPCLASSNSP# duplicates
0122.0122.00.00.00.019.01.90.015.10.00.00.00.039.0103.0142.01.00.0120.0120.0122.03.00.00.03.01.03
1123.0123.00.00.00.049.00.87.013.80.00.00.00.074.063.0137.02.00.0129.0127.0129.02.01.00.01.01.02
2123.0123.02.03.00.050.00.94.014.80.00.00.00.082.058.0140.07.00.0129.0128.0130.05.01.00.02.01.02
3123.0123.03.04.00.052.00.82.015.40.00.00.00.090.050.0140.07.00.0129.0128.0130.04.01.00.02.01.02
4135.0135.00.00.00.062.00.571.06.90.00.00.00.097.071.0168.03.00.0143.0142.0144.01.01.00.09.03.02
5140.0140.05.00.03.034.01.20.010.30.00.00.00.060.0119.0179.02.00.0156.0153.0155.05.00.00.02.01.02
6144.0144.00.015.00.076.00.461.010.60.00.00.00.081.071.0152.03.00.0145.0144.0146.02.01.01.010.02.02
7145.0145.00.013.00.077.00.245.05.80.00.00.00.021.0129.0150.01.00.0146.0145.0147.00.01.01.010.02.02
8146.0146.00.00.04.065.00.439.07.00.00.00.00.019.0137.0156.01.00.0150.0149.0151.01.01.01.010.02.02
9148.0148.02.00.01.040.00.90.010.60.00.00.00.035.0136.0171.01.00.0153.0155.0156.04.00.00.02.01.02